class_docstr: Emformer Encoder class for HuBERT pre-training. Consists of emformer module,
      linear layer and layer normalization layer.
  
  Args:
      emformer (torch.nn.Module):
          :py:class:`torchaudio.models.Emformer` module that consists of a list of emformer layers.
      output_linear (torch.nn.Module):
          Linear layer after emformer module.
      layer_norm (torch.nn.Module):
          Apply layer normalization to the output.
class_docstr_tok: ["Emformer","Encoder","class","for","HuBERT","pre-training",".","Consists","of","emformer","module",",","linear","layer","and","layer","normalization","layer",".","Args",":","emformer","(","torch.nn.Module",")",":",":","py",":","class",":","`","torchaudio.models.Emformer","`","module","that","consists","of","a","list","of","emformer","layers",".","output_linear","(","torch.nn.Module",")",":","Linear","layer","after","emformer","module",".","layer_norm","(","torch.nn.Module",")",":","Apply","layer","normalization","to","the","output","."]
code: def __init__(
      self,
      emformer: torch.nn.Module,
      output_linear: torch.nn.Module,
      layer_norm: torch.nn.Module,
  ):
      super().__init__()
      self.emformer = emformer
      self.output_linear = output_linear
      self.layer_norm = layer_norm
  
file_path: audio/src/torchaudio/prototype/models/_emformer_hubert.py
imports: from typing import List, Optional, Tuple
  import torch
  from torchaudio.models import Wav2Vec2Model
  from torchaudio.models.emformer import Emformer
  from torchaudio.models.rnnt import _TimeReduction
label_desc: Use image node_id 1 to create a new EmformerEncoder object from inherited base classes: torch.nn with example: obj = EmformerEncoder(emformer, output_linear, layer_norm)